Using road topology to improve cyclist path prediction

Conference paper (2017)

Authors

E.A.I. Pool Universiteit van Amsterdam
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
D. Gavrila Universiteit van Amsterdam, Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 E.A.I. Pool, J.F.P. Kooij, D. Gavrila
DOI
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https://doi.org/10.1109/IVS.2017.7995734
Tracking Topology Vehicle dynamics Predictive models Roads Layout Benchmark testing
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then spatially aligned to local curves and crossings in the road. We study a standard approach for path prediction in the literature based on Kalman Filters, as well as a mixture of specialized filters related to specific road orientations at junctions. Our experiments demonstrate an improved prediction accuracy (up to 20% on sharp turns) of mixing specialized motion models for canonical directions, and prior knowledge on the road topology. The new track data complements the existing video, disparity and annotation data of the original benchmark, and will be made publicly available.